基于入侵特征选择的网络异常数据检测模型

Network anomaly detection model based on intrusion feature selection

为了提高网络运动可靠性和安全性,针对传统的防火墙检测方法对网络异常数据检测准确性不高的问题,提出一种基于入侵特征选择的网络异常数据检测模型。对网络传输信道中的数据采用关联维求解方法进行特征挖掘提取,并对提取的关联维信息特征进行优选实现入侵信息识别和分类,结合模糊C均值聚类算法实现对网络异常数据的有效挖掘和检测。仿真结果表明,该检测模型能提高对网络异常数据和入侵信息的有效识别和检测能力。

Since the detection accuracy of the traditional firewall detection method for network abnormal data is not high,a novel network intrusion data detection model based on feature selection is proposed in this paper to improve the network reliability and security.The correlation dimension solution method is adopted to realize feature mining and extraction of information data in network channel.The extracted correlation dimension information features are optimized to achieve intrusion information identification and classification,and finally implement effective mining and detection of abnormal network data in combination with fuzzy C means clustering algorithm.The simulation results show that the proposed detection model can improve the effective identification and detection abilities to deal with network abnormal data and intrusion information.